For many military and industrial applications, intelligent sensor systems exist and are in development that recognize sensed signals and assist the user to interpret the signals. A wide variety of intelligent sensor systems compare data that the systems store in a memory to the signals that the sensor system detects. Uses for these types of systems may include, for example, automatic target recognition systems, environmental research and military satellites, industrial quality control devices, and medical laboratory research microscopes. These intelligent systems use a variety of sensing media including infrared radiation, visible light, ultraviolet radiation, radio frequency waves, x-rays and gamma radiation.
Common to all these intelligent sensor systems is the problem of relating the signals that the sensor detects to the data models in the system's computer memory. This problem becomes particularly difficult when the data model in memory is in high resolution and the sensor detects signals in low resolution. To date, no known method simply and reliably relates low resolution data models to high resolution data models that an intelligent sensor system may hold in a computer memory. Thus, there is a need for a method and system to assist intelligent sensor systems to simply and reliably relate high resolution data models in a system memory to low resolution signals that the system detects.
Some research in the field of artificial intelligence offers insight to the problem of relating high resolution signals to low resolution signals. A. P. Witkin, Space-Scale Filtering, PROCEEDINGS OF THE EIGHTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, August, 1983, pp. 1019-1022, indicates that it is possible to uniformly describe the maximum and minimum values, or extrema, of physical signals over a wide range of resolutions and to describe the extrema in a way that constrains and guides methods of characterizing the physical signals. In other words, Witkin shows that it is possible to create a uniform relationship between a physical signal and various resolutions at which the signal may be characterized. In particular, this work shows that as the resolution of a one-dimensional physical signal changes, the points at which the signal intersects a fixed zero crossing line through which the signal passes also changes in a regular way.
By applying these concepts, it is possible to understand that a difference in sensor-to-object distances between short-range signals and long-range signals changes the resolution in a regular way. If a method existed to simulate the way in which changes in sensor-to-object distances change short-range data models to produce long-range data models, then an intelligent sensor system incorporating this method could generate long-range data models from short-range data models. A sensor system capable of generating long-range data models to compare with long-range detected signals could more easily interpret detected signals than a system without such capability. There is, therefore, a need for a method that simulates the way in which changes in distance produce changes in resolution from short-range models to long-range models.
Often a sensor system detects multi-dimensional objects to produce multi-dimensional signals. For example, an infrared sensor system often senses three dimensional objects and produces a two-dimensional signal. The infrared sensor system describes the two-dimensional signal by horizontal and vertical point coordinates. The research of Witkin and others indicates that it is possible to work with one-dimensional physical signals to relate high resolution signals to low resolution signals. No one, however, has come forward with a method for relating multi-dimensional short-range data models to multi-dimensional long-range data models. There is a need for a method and system to relate multi-dimensional short-range models to multi-dimensional long-range models.
Consequently, a need exists for a method and system to relate disparate data models that have applications in a wide variety of intelligent sensor systems, including automatic target recognition systems, environmental research and military satellites, industrial quality control devices, and medical laboratory research microscopic sensors. There is also a need for a method and system that have applications in a variety of sensing media, including infrared radiation, visible light, ultraviolet waves, radio frequency waves, and x-rays and gamma rays.